icon for mcp server

Context Awesome

STDIOHTTP-SSE

MCP server providing access to 8500+ awesome lists with 1M+ curated resources for AI agents

context-awesome : awesome references for your agents

MCP Server

A Model Context Protocol (MCP) server that provides access to all the curated awesome lists and their items. It can provide the best resources for your agent from sections of the 8500+ awesome lists on github and more then 1mn+ (growing) awesome row items.

What are Awesome Lists? Awesome lists are community-curated collections of the best tools, libraries, and resources on any topic - from machine learning frameworks to design tools. By adding this MCP server, your AI agents get instant access to these high-quality, vetted resources instead of relying on random web searches.

Perfect for :

  1. Knowledge worker agents to get the most relevant references for their work
  2. The source for the best learning resources
  3. Deep research can quickly gather a lot of high quality resources for any topic.
  4. Search agents

https://github.com/user-attachments/assets/babab991-e4ff-4433-bdb7-eb7032e9cd11

Available Tools

1. find_awesome_section

Discovers sections and categories across awesome lists matching your search query.

Parameters:

  • query (required): Search terms for finding sections
  • confidence (optional): Minimum confidence score (0-1, default: 0.3)
  • limit (optional): Maximum sections to return (1-50, default: 10)

Example Usage: "Give me the best machine learning resources for learning ML related to python in couple of months." "What are the best resources for authoring technical books ?" "Find awesome list sections about React hooks" "Search for database ORMs in Go awesome lists"

2. get_awesome_items

Retrieves items from a specific list or section with token limiting for optimal context usage.

Parameters:

  • listId or githubRepo (one required): Identifier for the list
  • section (optional): Category/section name to filter
  • subcategory (optional): Subcategory to filter
  • tokens (optional): Maximum tokens to return (min: 1000, default: 10000)
  • offset (optional): Pagination offset (default: 0)

Example Usage:

"Show me the testing tools section from awesome-rust"
"Get the next 20 items from awesome-python (offset: 20)"
"Get items from bh-rat/awesome-mcp-enterprise"

Installation

Remote Server (Recommended)

Context Awesome is available as a hosted MCP server. No installation required!

Install in Cursor

Go to: Settings -> Cursor Settings -> MCP -> Add new global MCP server

{ "mcpServers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Claude Code
claude mcp add --transport http context-awesome https://www.context-awesome.com/api/mcp
Install in Windsurf
{ "mcpServers": { "context-awesome": { "serverUrl": "https://www.context-awesome.com/api/mcp" } } }
Install in VS Code
"mcp": { "servers": { "context-awesome": { "type": "http", "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Claude Desktop

Navigate to Settings > Connectors > Add Custom Connector. Enter:

  • Name: Context Awesome
  • URL: https://www.context-awesome.com/api/mcp

See Additional Installation Methods for other MCP clients.

Local Setup

For development or self-hosting:

git clone https://github.com/bh-rat/context-awesome.git cd context-awesome npm install npm run build

Configuration

Running the Server

# Development mode (runs from source) npm run dev -- [options] # Production mode (runs compiled version) npm run start -- [options] Options: --transport <stdio|http|sse> Transport mechanism (default: stdio) --port <number> Port for HTTP transport (default: 3000) --api-host <url> Backend API host (default: https://api.context-awesome.com) --debug Enable debug logging --help Show help

Examples

# Run with default settings (stdio transport) npm run start # Run with HTTP transport on port 3001 npm run start -- --transport http --port 3001 # Run with custom API host and key npm run start -- --api-host https://api.context-awesome.com

MCP Client Configuration

Claude Desktop

Add to your Claude Desktop configuration file:

{ "mcpServers": { "context-awesome": { "command": "node", "args": ["/path/to/context-awesome/build/index.js"], "env": { "CONTEXT_AWESOME_API_HOST": "https://api.context-awesome.com" } } } }
Cursor/VS Code

Add to your settings:

{ "mcpServers": { "context-awesome": { "command": "node", "args": ["/path/to/context-awesome/build/index.js"], "env": { "CONTEXT_AWESOME_API_HOST": "https://api.context-awesome.com" } } } }
Custom Integration

For HTTP transport:

npm run start -- --transport http --port 3001 --api-host https://api.context-awesome.com

Then configure your client to connect to http://localhost:3001/mcp

Testing

With MCP Inspector

npm run inspector

Debug Mode

Enable debug logging to see detailed information:

npm run start -- --debug # Or in development mode npm run dev -- --debug

Additional Installation Methods

Install in Cline
{ "mcpServers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Zed
{ "context_servers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Augment Code
  1. Click the hamburger menu
  2. Select Settings
  3. Navigate to Tools
  4. Click + Add MCP
  5. Enter URL: https://www.context-awesome.com/api/mcp
  6. Name: Context Awesome
Install in Roo Code
{ "mcpServers": { "context-awesome": { "type": "streamable-http", "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Gemini CLI
{ "mcpServers": { "context-awesome": { "httpUrl": "https://www.context-awesome.com/api/mcp" } } }
Install in Opencode
"mcp": { "context-awesome": { "type": "remote", "url": "https://www.context-awesome.com/api/mcp", "enabled": true } }
Install in JetBrains AI Assistant
  1. Go to Settings -> Tools -> AI Assistant -> Model Context Protocol (MCP)
  2. Click + Add
  3. Configure URL: https://www.context-awesome.com/api/mcp
  4. Click OK and Apply
Install in Kiro
  1. Navigate Kiro > MCP Servers
  2. Click + Add
  3. Configure URL: https://www.context-awesome.com/api/mcp
  4. Click Save
Install in Trae
{ "mcpServers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Amazon Q Developer CLI
{ "mcpServers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Warp
  1. Navigate Settings > AI > Manage MCP servers
  2. Click + Add
  3. Configure URL: https://www.context-awesome.com/api/mcp
  4. Click Save
Install in Copilot Coding Agent
{ "mcpServers": { "context-awesome": { "type": "http", "url": "https://www.context-awesome.com/api/mcp", "tools": ["find_awesome_section", "get_awesome_items"] } } }
Install in LM Studio
  1. Navigate to Program > Install > Edit mcp.json
  2. Add:
{ "mcpServers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }
Install in BoltAI
{ "mcpServers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Perplexity Desktop
  1. Navigate Perplexity > Settings
  2. Select Connectors
  3. Click Add Connector
  4. Select Advanced
  5. Enter Name: Context Awesome
  6. Enter URL: https://www.context-awesome.com/api/mcp
Install in Visual Studio 2022
{ "inputs": [], "servers": { "context-awesome": { "type": "http", "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Crush
{ "$schema": "https://charm.land/crush.json", "mcp": { "context-awesome": { "type": "http", "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Rovo Dev CLI
acli rovodev mcp

Then add:

{ "mcpServers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }
Install in Zencoder
  1. Go to Zencoder menu (...)
  2. Select Agent tools
  3. Click Add custom MCP
  4. Name: Context Awesome
  5. URL: https://www.context-awesome.com/api/mcp
Install in Qodo Gen
  1. Open Qodo Gen chat panel
  2. Click Connect more tools
  3. Click + Add new MCP
  4. Add:
{ "mcpServers": { "context-awesome": { "url": "https://www.context-awesome.com/api/mcp" } } }

Backend service

This MCP server connects to backend API service that handles the heavy lifting of awesome list processing.

The backend service will be open-sourced soon, enabling the community to contribute to and benefit from the complete context-awesome ecosystem.

License

MIT

Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Add tests for new functionality
  4. Ensure all tests pass
  5. Submit a pull request

Support

For issues and questions:

Attribution

This project uses data from over 8,500 awesome lists on GitHub. See ATTRIBUTION.md for a complete list of all repositories whose data is included.

Credits

Built with:

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